Chemical detection system with at least one electronic nose

ABSTRACT

A system for predicting one or more analytes based on outputs from thin film gas sensors is provided. The system may comprise an electronic nose (e-nose). The e-nose may comprise the gas sensors and a first processor. The system may further comprise a second processor. The second processor may be configured to receive the output from each of the gas sensors, evaluate a prediction accuracy using an evaluation parameter of each of a plurality of models which are trained and tested and select a model from among the plurality of models to deploy based on a comparison of the evaluation parameter for each of the plurality of models and use the same. The second processor may also receive, an output of each of the gas sensors caused by unknown one or more analytes; and predict, using the deployed model, the one or more analytes that causes the output.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalApplication Ser. No. 63/081,959 filed on Sep. 23, 2020 and U.S.Provisional Application Ser. No. 63/081,962 filed on Sep. 23, 2020, theentirety of which are incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

The United States Government has rights in this invention pursuant tocontract no. DE-AC05-00OR22725 between the United States Department ofEnergy and UT-Battelle, LLC.

FIELD OF THE DISCLOSURE

This disclosure relates to systems with at least one electronic nosewith a plurality of gas sensors.

BACKGROUND

Aromas are present as an important characteristic of all natural andman-made products and their manufacturing, life of products providingunique distinct way of characterizing the owner and object. Aromas maybe a sign of health, freshness of food or beverages (e.g., tea, coffee,etc.), quality of manufactured materials, as well as a sign of danger orimminent threat, as for example, the aroma may be from a toxic chemical.Therefore, correctly identifying, classifying and quantifying an aromasuch that a person can understand it is important in various differentapplications.

SUMMARY

Accordingly, disclosed is a system for predicting one or more analytesbased on outputs from a plurality of thin film gas sensors. The systemmay comprise an electronic nose (e-nose). The e-nose may comprise thethin film gas sensors and a first processor. The first processor may beconfigured to supply power to the plurality of thin film gas sensors tobias the sensors and receive output from each of the plurality of thinfilm gas sensors. The system may further comprise a second processor.The second processor may be configured to receive the output from eachof the plurality of thin film gas sensors, generate randomly a firstdataset for training and a second dataset for testing a plurality ofmodels using the received output, train and test the plurality of modelsusing one or more combinations of outputs from the plurality of thinfilm gas sensors, evaluate a prediction accuracy of each of theplurality of models using an evaluation parameter and select a modelfrom among the plurality of models to deploy for detecting analytesbased on a comparison of the evaluation parameter for each of theplurality of models. The second processor may also receive, an output ofeach of the plurality of thin film gas sensors caused by unknown one ormore analytes; and predict, using the deployed model, the one or moreanalytes that causes the output.

The output from each of the plurality of thin film gas sensors may be inresponse to different analytes separately positioned near the pluralityof thin film gas sensors, respectively, one at a time, and differentcombinations of analytes positioned near the plurality of thin film gassensors, respectively, one at a time. The plurality of models may begenerated using a plurality of different machine learning techniques.The training may be based on the first dataset and the testing based onthe second dataset.

In some aspects, the second processor may be configured to predict,using the deployed model, the concentrations of the one or more analytesthat causes the output. In some aspects, the deployed model forpredicting the concentrations may be different from the deployed modelfor predicting the one or more analytes.

In some aspects, the second processor may be the same as the firstprocessor.

Also disclosed is an additive manufacturing system. The system maycomprise at least one electronic nose (e-nose). The e-nose may comprisea housing having openings on corresponding ends thereof to enable airflow, a plurality of thin film gas sensor; and a mount configured tomount the housing to an extruder head of an additive manufacturingdevice. The system may also further comprise a processor. The processormay be configured to supply power to the plurality of thin film gassensors to bias the sensors, receive output from each of the pluralityof thin film gas sensors, determine whether there is an abnormality inan additive manufacturing process manufacturing a product from one ormore materials based on one or more combinations of output from theplurality of thin film gas sensors during the additive manufacturingprocess and a deployed machine learning model and generate a report forthe additive manufacturing process containing the determination.

The housing may have an air channel for air to flow between the ends.The active sensor portion of each gas sensor is in the air channel to beexposed to the air flow.

In an aspect of the disclosure, the abnormality may be based on apredicted decomposition level determined from the output and thedeployed machine learning model.

In an aspect of the disclosure, the additive manufacturing process maybe stopped depending on the abnormality.

Also disclosed is a system for determining an age and/or quality of foodor beverage. The system may comprise an electronic nose (e-nose). Thee-nose may comprise a housing, a plurality of thin film gas sensors, atleast one of an identification scanner, touch panel or image processorand a processor. The housing may have openings on corresponding endsthereof to enable air flow. The housing may have an air channel for airto flow between the ends. The active sensor portion of each gas sensormay be in the air channel to be exposed to the air flow. Theidentification scanner may be configured to read an identification codeof a food or beverage. The touch panel may be configured to receive userinput identifying the food or beverage. The image processor may beconfigured to analyze an acquired image of the food or beverage andidentify the food or beverage. The processor may be configured to supplypower to the plurality of thin film gas sensors to bias the sensors andreceive output from each of the plurality of thin film gas sensors,predict the age and/or quality of the food or beverage based on one ormore combinations of outputs from the plurality of thin film gas sensorsand a deployed machine learning model and issue a notification of thedetermination.

In an aspect of the disclosure, the processor may be configured todetermine that the food or beverage item or combination of items hasexpired when a predicted age correlates an age associated with a spoiledcondition or is older than an age associated with spoiled condition.

In an aspect of the disclosure, the processor may be configured todetermine the age of the food or beverage item, or combination of itemsbased on a deployed machine learning model determined from images of theitem or combination of items.

Also disclosed is a system for predicting one or more natural languagedescriptors associated with an aroma of an item. The system may comprisean electronic nose (e-nose). The e-nose may comprise a housing, aplurality of thin film has sensors and a processor. The housing may haveopenings on corresponding ends thereof to enable air flow. The housingmay have an air channel for air to flow between ends. The active sensorportion of each gas sensor may be may be in the air channel to beexposed to the air flow. The processor may be configured to supply powerto the plurality of thin film gas sensors to bias the sensors andreceive output from each of the plurality of thin film gas sensors,calculate one or more ratios of the outputs of the plurality of thinfilm gas sensors, predict the one or more natural language descriptorsusing a logistic regression model using inputs of one or more outputs ofthe plurality of thin film gas sensors and the calculated one or moreratios; and output results of prediction.

In an aspect of the disclosure, the prediction may include a confidence.

In an aspect of the disclosure, the processor may further predict apercent depletion of the aroma.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system in accordance with aspects of thedisclosure;

FIG. 2 is a diagram of a single board computer in accordance withaspects of the disclosure;

FIG. 3A and FIG. 3B are diagrams showing an example of a mountingbracket for mounting to an exhaust or duct in accordance with aspects ofthe disclosure;

FIG. 4 is a diagram in accordance with aspects of the disclosure;

FIG. 5A is a diagram showing the mounting location for the sensor unitin accordance with aspects of the disclosure, FIG. 5B shows openings orholes in the air flow passage in accordance with aspects of thedisclosure and FIG. 5C shows a view of the mounting bracket in sections;

FIG. 6 is a diagram of an example of a graphical user interface inaccordance with aspects of the disclosure;

FIGS. 7 and 8 are diagrams of another system in accordance with aspectsof the disclosure;

FIG. 9 is a diagram of another system in accordance with aspects of thedisclosure;

FIG. 10 is a flow chart illustrating a method for deploying a model(s)in accordance with aspects of the disclosure;

FIG. 11 is a flow chart illustrating a method in accordance with aspectsof the disclosure;

FIG. 12 is a flow chart illustrating another method in accordance withaspects of the disclosure;

FIG. 13 is a block diagram of another system in accordance with aspectsof the disclosure;

FIG. 14 is a diagram of an example of a table for generating a qualityreport for an additive manufacturing process in accordance with aspectsof the disclosure;

FIG. 15 is a flow chart illustrating a method in accordance with aspectsof the disclosure;

FIG. 16 is a diagram of another system in accordance with aspects of thedisclosure;

FIG. 17 is a flow chart illustrating a method in accordance with aspectsof the disclosure;

FIG. 18 is an example of a cluster of sensor output ratios in accordancewith aspects of the disclosure;

FIGS. 19A-19I are example graphs of different natural languagedescriptors and measured sensor responses from different hops inaccordance with aspects of the disclosure, where FIGS. 19A, 19D and 19Gshow relationships between pairs of sensor outputs and the differenthops, FIGS. 19B, 19E and FIG. 19H show the relationships between pairsof sensor outputs and the different natural language descriptors andFIGS. 19C, 19F and 19I shown clusters the relationships between pairs ofsensor outputs and clusters of natural language descriptors;

FIG. 20 is a flow chart illustrating a method in accordance with aspectsof the disclosure; and

FIG. 21 is an example of sensor output at different times for a sampleand determination of partial aroma depletion in accordance with aspectsof the disclosure.

DETAILED DESCRIPTION

Chemical Spillage Detection

FIG. 1 is diagram of a chemical detection and alert system 1 inaccordance with aspects of the disclosure. The system 1 may be used todetect the type and/or concentration of chemicals and generate a warningor alert based on the detection. The system 1 may be used in anylaboratory, factory or storage facility having chemicals.

The system 1 may comprise a sensor unit 10 having a sensor array 12. Thesensor array may comprise a plurality of gas sensors. Each gas sensorhas a measurable response in the presence of one or more gases, such aschemicals (also referred to herein an analytes). In some aspects of thedisclosure, the measurable response is due to a change in resistance,which is measured by a voltage.

The number of the gas sensors in the sensor array 12 may be based on aparticular application, such as a specific laboratory that only usescertain chemicals or a particular storage cabinet which is only used forcertain chemicals. In some aspects of the disclosure, the gas sensorsmay be thin film gas sensors. For example, the sensors may be made ofSnO₂. The nominal analyte selectivity of a sensor is designated by asensor manufacturer. Each sensor may be tuned to be selectivity(sensitivity) to specific analytes. However, by combining the outputs asdescribed herein (to generate a sensor pattern or output pattern), thesensor array 12 has extended capabilities and may be used to identifyconcentrations of analytes where each sensor is not specifically tunedto using one or more aspects of the disclosure such as through thetraining and testing of machine learning models and selection of onemodel from among a plurality of models (which will be described later indetail).

The sensors may be selected from MQ-2, MQ-4, MQ5 and MQ-7 sensors. Forexample, an MQ-2 gas sensor may be used for gas leakage detection suchas H₂, LPG, CH₄, C₃H₈, CO, Alcohol, Smoke or Propane. An MQ-4 gas sensormay be used to detect analytes such as CH₄, alcohol, smoke. An MQ-5 gassensor may be used to detect H₂, LPG, CH₄, CO, Alcohol. An MQ-7 gassensor may be used to detect CO. In some aspects of the disclosure, asshown in FIG. 4, there may be nine gas sensors in the sensor array 12.

The gas sensors are not limited to the above, and other volatile organiccompound sensors (VOX) sensors and inorganic compound sensors may beused including those on flexible substrate. The gas sensors may bepositioned within an air flow in order to detect the analyte in the airflow.

In accordance with aspects of the disclosure, the system 1 may detectthe concentration of the analyte in the air flow using one or morecombinations of the outputs of the air sensors in the sensor array 12(sensor patterns).

The sensor unit 10 may also comprise analog-digital converters 14 (ADC).The ADC 14 may be configured to convert the analog signals from thesensor array 12 to digital signals for further processing. In an aspectof the disclosure, each gas sensor may have its own ADC. The ADC 14 maybe an integrated circuit as in part no. MCP3008. Each MCP3008 canconvert up to eight analog signals into digital signals, respectively.Thus, when more than eight gas sensors (and other sensors) may be used,more than one MCP3008 may be needed. The ADC 14 may be attached to apower board. The power board may be connected to the sensor array 12 viaa flexible connector (such as a 25 pin connector).

The sensor unit 10 may also comprise a single board computer 20. Thesingle board computer 20 may be connected to the ADC 14 via the powerboard (via a flexible connector). The single board computer 20 may be aRaspberry PI (Raspberry Pi (Trading) Limited). The single board computer20 causes power to be supplied to the sensor array 12 (as needed). Powermay be supplied continuously or periodically based on user selection.The single board computer 20 also receives the digital signals from theADC 14 and may store the digital signals locally. In an aspect of thedisclosure, the data may be stored in some form of memory for postprocessing. For example, in some aspects, the data may be stored in aCSV format (and timestamped). Additionally, in some aspects of thedisclosure, the single board computer 20 may calculate an average ofindividual outputs from the sensors and store the average. In someaspects, the single board computer 20 transmits the output from thesensors (digital version) to a server (processing system 100) forfurther processing. In other aspects, the single board computer 10 maytransmit the calculated average of a selected number of outputs (digitalversion) from each sensor to the server (processing system 100).

The transmission may be via wireless communication. In other aspects,the transmission may be via a wired network.

FIG. 2 shows a block diagram of the single board computer 20 inaccordance with aspects of the disclosure. The single board computer 20may comprise a processor 200, a memory 202, wireless communicationinterfaces 204A/204B, a power supply interface 206, an HDMI interface208 (optional) and one or more USB interfaces 210 (optional).

The processor 200 may be a microprocessor. The memory 202 may includeRandom Access Memory (RAM). In other aspects of the disclosure, thememory 202 may also include Read Only Memory (ROM). The ROM may storeone or more programs such as a client program, when executed causes theprocessor 200 to execute the functionality described herein (such as,but not limited to, control of a switch based on user selection,calculating of averages based on user selection, periodically orcontinuation transmitting digital versions of outputs of the gas sensorsto the processing system 100 (server)).

The wireless communication interfaces may be interfaces for WI-FI(Trademark) 204A and Bluetooth (Trademark) 204B. In an aspect of thedisclosure, the single board computer 20 may communicate with theprocessing system 100 via the WI-FI interface 204A. In other aspects ofthe disclosure, information in the memory 202 may be transmitted to alocal reader via the Bluetooth interface 204B. The local reader may beconnected to the processing system 100 and upload the data. In otheraspects of the disclosure, information in the memory 202 may betransmitted to a local reader via another wireless interface.

In other aspects of the disclosure, the single board computer 20 maycomprise a wired network communication interface such as Ethernet. Thesingle board computer 20 may communicate with the processing system 100via the wired network (using an Ethernet cable).

The HDMI interface 208 or other display interface may be used to attacha display to view the sensor outputs.

The power supply interface may be a USB interface (such as USB-3). Inother aspects of the disclosure, the power supply interface may be abarrel jack connector. In other aspects, the power supply interface maybe connected to a power outlet or a standalone power source such as anexternal battery.

The gas sensors draw a significant amount of current to heat up (inorder for sensing to be reversible). In accordance with aspects of thedisclosure, the gas sensors may receive the power from a 5V, 4A barreljack (power 16). The barrel jack forms a shared power bus for the gassensors and may be used to power the single board computer 20. Thiseliminates a need to have the USB interface 210 to power the singleboard computer 20. In an aspect of disclosure, the barrel jack may besoldered to the same board as the ADC 14. In other aspects of thedisclosure, the power for the system 1 may be solar power and thesensing unit 10 may have a solar cell and be positioned to capturelight.

In an aspect of the disclosure, the sensor unit 10 may also comprise aswitch 18. The switch 18 may be a N-channel MOSFET. This switch 18 maybe controlled by the single board computer 20 (processor 200) based onuser selection (setting). Advantageously, by using the switch 18,electricity is not wasted when the gas sensors are not actively beingused and needed. Not shown in FIG. 1, a rectifier diode and pull-downresistor may be connected to the N-channel MOSFET. The resistormaintains the MOSFET OFF by default. The rectifier diode prevents anyback electromotive force (EMF) caused by the sensor array 12 (pluralityof gas sensors).

Also not shown in FIG. 1, the sensor unit 10 may also comprise othersensors such as a humidity sensor, a pressure sensor and a temperaturesensor (particle detector or dust sensor). These sensors may be used forcalibration and adjustment of the output signals from the gas sensors,as needed.

These other sensors do not use a significant amount of power andtherefore, may be directly connected to the single board computer 20.

An active sensor portion (such as the active surface or sensor head) 525of each sensor in the sensor array 12 is positioned in air flow passage.The air flow passage may be, but is not limited to, a hood, an exhaust,an air duct in a building such as a laboratory. In an aspect of thedisclosure, the active sensor portion 525 of each sensor may bepositioned in the air flow passage through a mounting bracket 300. Themounting bracket 300 may be attached to or connected to a duct, pipe,exhaust, or any path of an air flow in a controllable manner. The airflow passage may have corresponding openings or holes 530 as shown inFIG. 5B to receive the active sensor portion 525. The air flow passagemay have different shapes such as circular, oval, rectangular. Thebracket 300 may mimic the shape of the exterior of the air flow passageto assure leak free mounting of the active sensor portion.

FIGS. 3A and 3B depict different views of a mounting bracket 300. Themounting bracket 300 may be fabricated using additive manufacturing suchas 3D printing. The dimensions may be based on the dimensions of the airflow path. While the mounting bracket 300 is shown in FIGS. 3A and 3B astubular, as noted above, the bracket 300 may have other shapes.

As shown in FIGS. 3A and 3B, mounting bracket 300 also has openings 305one for each gas sensor. The active sensor portion 525 may be connectedto its corresponding circuitry which may be in a box 530 via the openingin the bracket. The electronic circuitry of each sensor may be mountedon the exterior air flow passage and the bracket 300 exterior such asshown in FIG. 5C to protect it from potentially harsh environmentsinside the air flow passage which may lead to damage of electroniccircuitry. The footprint of the active sensor portion 525 inside the airflow passage should be minimal to reduce formation of stagnation zonesabove the sensor and restriction of the air flow. In other aspects,instead of openings 305, the mounting bracket 300 may have recessesforming compartments for the sensor body, e.g., circuitry, such that theactive sensor portion 525 may be inside the air flow passage when themounting bracket 300 is attached to the air flow passage. While FIGS. 3Aand 3B show the mounting bracket 300 as a complete tube, the bracket 300may be formed from multiple parts as shown in FIG. 5C such that it maybe clamped to the air flow passage.

In other aspects, instead of a mounting bracket 300, a part of the airflow passage may be replaced with an assembly. The assembly may havecompartments or recesses such that the gas sensors may be positioned inthe same to enable the active sensor portion 525 to face the air flowpassage and be in the air flow. In other aspects, the assembly may haveopenings for each sensor and the sensors may be positioned in arespective opening such that the active sensor portion 525 may bepositioned in the air flow. The assembly may be attached to theremaining portion of the air flow passage via welding or other knownmeans of attachment.

In an aspect of the disclosure, the mounting bracket 300 may alsoinclude additional attachment points 320 for other sensors such as aparticle detection mount. While in FIG. 3B, the particle sensor 400 maybe attached to the inner surface of the bracket 300, in other aspects,the bracket may have an additional opening or recess (compartment) forthe particle sensor 400. The opening or recess (compartment) may have asimilar function, e.g., to hold the particle sensor 400 such that theactive sensor portion (surface) is within the air flow passage (whilecertain circuitry is external).

In other aspects, the bracket 300 or assembly may have supports orprojections 300A for mounting each sensor such as shown in FIG. 4.

In FIG. 4, nine gas sensors are mounted on projections 305A from theinner wall of the bracket 300. The particle detector 400 is attached tothe mounting bracket 300. A power board with the jack and the ADC 14 isconnected to the sensor array 12 using flexible connectors with pins (25pin connector). The single board computer 20 is connected to the powerboard with a flexible connector.

As described above, the mounting bracket 300 is intended to be mountedin an air flow passage by fitting over a vent or exhaust dust 510 andattaching by an attachment means as described above. FIG. 5A illustratesan example of a mounting location for the mounting bracket 300 in abuilding such as a laboratory. In an aspect of the disclosure, theprocessing system 100 may be mounted to the outside of the vent orexhaust dust. In other aspects, the processing system 100 may also bemounted to a wall near the vent or exhaust dust 510. Additionally, thesingle board computer 20, ADC 14 and power 16 may be mounted to theoutside of the vent or exhaust dust 510 and located on a wall near thesame. FIG. 5A illustrates that the mounting bracket 300 is connected toa duct or an exhaust 510 for a laboratory (chemical storage). In thisaspect of the disclosure, the air is moving via forced air 505 such aspart of a ventilation system including an HVAC system. The air may becirculated via one or more fans.

In an aspect of the disclosure, the mounting bracket 300 may be in acentral duct 510 that receives air flow from multiple rooms 520 such asshown in FIG. 5A. However, in other aspects of the disclosure, themounting bracket 300 may be in an individual room duct or exhaust. Inthis aspect of the disclosure, multiple sensor units 10 may be deployedsuch as shown in FIG. 9.

FIG. 6 illustrates an example of a graphical user interface 600 (GUI)which may be used to select/set sensing parameters. The sensingparameters may include continuous, periodic or aperiodic. If periodic,the parameters may include frequency, such as each minute, 5 minutes, 10minutes, 30 minutes, 1 hour . . . . In an aspect of the disclosure, theperiod may be the same for each day. Alternatively, the frequency mayvary depending on the time of day or the day of the week. For example,during normal work hours, the period may be shorter than overnight.Additionally, the period may be shorter during the weekday than theweekend.

The GUI 600 may also be used to set whether averaging of sensor outputmay be used in the processing. For example, the user may set that thesensor output is averaged using a plurality of readings such as 2, 5,10, 25 . . . etc. When averaging is set, the processor 200 may calculatethe average of the different outputs (from the same gas sensor) prior totransmission to the processing system 100 (server).

In other aspects of the disclosure, the GUI 600 may be used to view theoutputs from each gas sensor (and any other sensor). In an aspect of thedisclosure, the single board computer 20 may transmit the sensor outputto a device running the GUI 600. The GUI 600 may be executed on anydevice connectable to a network. For example, the device may be apersonal computer, a mobile device such as a mobile phone, tablet,laptop, etc. In other aspects, the device may communicate with theprocessing system 100 and obtain the sensor output for display on theGUI 600 from the processing system 100.

Referring back to FIG. 1, the system 1 may further comprise a processingsystem 100. The processing system 100 may act as a server for the singleboard computer 20. The processing system 100 may comprise a processor102, a memory 104, a wireless communication interface 106 andcommunication interface 108.

For example, the processor 102 may be a CPU. In other aspects, theprocessor 102 may be a microcontroller or microprocessor or any otherprocessing hardware such as a FPGA. The processor may be configured toexecute one or more programs stored in a memory 104 to execute thefunctionality described herein.

The memory 104 may be ROM and RAM. The memory may be any piece ofhardware capable of temporarily or permanently storing data. Thewireless communication interface 106 may be a WI-FI (trademark)interface. The wireless communication interface may communicate with thesensor unit 10 (single board computer 20). The communication may bebi-directional.

The communication interface 108 may be a wired communication interfacesuch as Ethernet. The processing system 100 may communicate with anothersystem via the communication interface 108. For example, the processingsystem 100 may communicate with a security system to provide an alertthat a chemical has been spilled. The alert may include a time of thespillage, e.g., timestamp of the sensor output that indicated aspillage, the chemical (analyte) and concentration. The concentrationand analyte being determined from one or more combinations of sensoroutputs and a deployed machine learning model.

In an aspect of the disclosure, the processor 102 is configured toexecute machine learning to create a model for concentrations ofanalyte. The model is trained and tested using a dataset. In an aspectof the disclosure, the model may be updated when a new analyte is added(e.g., training/testing repeated). The dataset may be stored in thememory 104 and in some aspects, in a CSV format.

FIGS. 7 and 8 depict another system 1A in accordance with aspects of thedisclosure. Instead of being mounted to a duct or exhaust (or hood), thesystem 1A may be portable and have a handle (not shown). In otheraspects of the disclosure, the housing 850 of the system 1A may bemounted to a wall in a room or a wall of a chemical storage cabinet (ora rack). The housing 850 may be mounted to a wall using brackets or arailing system. In other aspects of the disclosure, the housing 850 maybe mounted to a wall via an adhesive. The housing 850 is mounted in theorientation as shown in FIG. 8 such that the longitudinal axis isparallel to the direction of the air flow 805.

In an aspect of the disclosure, the system 1A may be placed on a base orstand such that the system 1A may to located on a desk or test bench,shelf, etc. The base or stand may be hollow in the center to allow airflow to enter the system 1A from the bottom.

In an aspect of the disclosure, the system 1A may be handheld and ahandle may be attached to the housing 850 to enable a user to hold thesystem 1A. In an aspect of the disclosure, the system 1A may be mountedto a ceiling, where the ceiling has an opening or a vent.

The housing 850 has an opening on the bottom and a corresponding openingon the top. The opening allows air to flow into the system 1A from thebottom and leave the system 1A at the top (the direction of the air flowis shown in FIG. 8 with arrows). The system 1A may comprise a verticalair channel 800. The air channel 800 may be a pipe such as a PVC pipe.The shape of the opening where air flows to the air channel 800 may beround, oval rectangular or any other shape to match the shape of the airchannel 800. As described above, a mounting bracket 300 or assembly maybe used having a plurality of sensors attached or inserted such that theactive sensor portion (surface) 525 of each sensor of sensor array 12 isexposed to the air flow (however the circuitry may be outside the airflow passage, e.g., air channel 800 such as contained in a box 530). Thesensors which work at elevated temperatures may be mounted above sensorswhich operate at ambient temperatures to assure adequate air samplinginside the air flow passage (e.g., air channel 800). If more than onesensor in the sensor array 12 operates at high temperature (T>ambient),the position of such sensors may be distributed evenly around the airchannel 800. In some aspects, the air channel 800 may have a pluralityof recesses. When sensors having a flexible substrate used, the sensorsmay be mounted in recesses, such that the flexible substrate is flashedwith inner part of the air channel 800 to reduce restriction to airflow. The sensors with electronic control boards (circuitry) may bedetached from the electronic boards and mounted through small holes onthe air channel 800 using ring mounting racket (for cylindrical pipe).In an aspect of the disclosure, electronic boards (circuitry) of thesensors may be assembled inside the environment protected box.

In other aspects of the disclosure, the air channel 800 may befabricated via additive manufacturing. In other aspects, instead of abracket 300, similar to described above, the air channel 800 mayopenings on the side which functions as an access holes for elements ofsensor array 12 such that the active sensor portion 525 faces the airflow and is exposed to the air flow such the circuitry may be externalto the air flow (mounted within the open or external to the air channel800). Sensor access openings may be evenly distributed over thediameter. If more sensors are accommodated than can be integrated on theperimeter of the air channel 800, several air channels 800 may beimplemented.

As in system 1, the sensor array 12 may comprise a plurality of gassensors. The number of gas sensors in the sensor array 12 may depend onthe application. In some aspects, where the system 1A is portable orwall mount in a particular room or storage cabinet, the number of gassensors in the sensor array 12 may be less than the number where the gassensors are located within the duct or exhaust. For example, in anaspect of the disclosure, the sensor array 12 may have four gas sensors.The system 1 may also have other sensors such as a particle detector(sensor).

In an aspect of the disclosure, since the gas sensors are heated suchthat the sensing is reversible, the heat of the gas sensors within theair channel 800 naturally induces movement of the air within the airchannel 800 due to convention. Therefore, there is no need for anaddition source to cause the air to move within the air channel 800 suchas a fan to force air motion.

The system 1A may also comprise a power board having an ADC 14, externalpower 16 and one or switches 18. Similar to above, the gas sensors mayreceive the power from a 5V, 4A barrel jack (power 16). The ADC 14 maybe an integrated circuit as in part no. MCP3008. Each MCP3008 canconvert up to eight analog signals into digital signals, respectively.The switches 18 may be MOSFETs. Each gas sensor may have its own switch,or one switch may be used for all of the gas sensors in the sensor array12.

The system 1A may also comprise a single board computer 20A. In anaspect of the disclosure, the single board computer 20A may be a CoralDev Board available from Google (Trademark), LLC. In accordance withthis aspect of the disclosure, the single board computer 20A may executetraining, testing and deployment of a machine learning model from amonga plurality of models, and determine concentration based on aspects ofthe disclosure instead of the processing system 100. The Coral Dev Boardhas an on-board Edge TPI coprocessor that is capable of performing highspeed ML. The single board computer 20A may receive power from anexternal power source such as via a USB-C connection. In other aspects,the single board computer 20A may receive power from the power board(5V, 4A barrel jack).

The system 1A may further comprise a camera system 700. In an aspect ofthe disclosure, the camera system 700 may be configured to take stillimages and/or moving images. The single board computer 20A may compriseone or more video interfaces such as s HDMI 2.0 or FFC connectors. In anaspect of the disclosure, the system 1A may further comprises a switch19 (such in FIG. 7) between the single board computer 20A and the camerasystem 700. The switch 19 may also be a MOSFET. The single boardcomputer 20A may control the switch 19 based on a determination from oneor more combinations of outputs from the gas sensor (e.g., based ondetermined concentration(s)). For example, when a determinedconcentration exceeds a threshold, the single board computer 20A maycontrol the switch to close and power the camera system 700 and enablethe camera system 700 to record still or moving images. This providesvisual data of who is in the room when the concentration exceeds thethreshold (evidence and tracing).

In an aspect of the disclosure, the system 1A may further comprise anotification device such as a speaker or light (LED) which emits anotification sound or light, respectively, when an event is determined,e.g., concentration exceeds a threshold. In some aspects of thedisclosure, the system 1A may further comprise a display. The displaymay display a warning such as indicating the concentration detected, theanalyte type and a timestamp of the time of detection (or time theoutput from gas sensors was received which triggered the determination).In an aspect of the disclosure, the display may be attached to orembedded in the housing 850. The display may be connected to a videoconnector. The speaker may be connected to an audio jack or terminal ofthe single board computer 20A. When a display is used, the single boardcomputer 20A may cause the display to display the video or stillimage(s) taken by the camera system 700.

The single board computer 20A may also include similar components asdescribed above and shown in FIG. 2, e.g., processor, memory, wirelesscommunication interfaces and other communication interfaces. The memorymay comprise programs for causes the single board computer 20A toexecute the functionality described herein including a plurality ofdifferent machine learning algorithms (including for training, testingand deployment of a model), applying a trained/tested model to the gassensor outputs, updating the models and deployment as needed. The memorymay also comprise concentration thresholds used to determine whether toissue an alert or notification or activate the camera system 700. Thememory may also include the output from the gas sensors, the determinedconcentrations, timestamps (associated with the outputs andconcentrations) and video/still images obtained by the camera system700.

In other aspects of the disclosure, instead of or in addition to theabove notification device(s), the system 1A may transmit an alert to asecurity system or another device. The alert may comprise the analytetype (chemical), the concentration detected and a timestamp. In otheraspects of the disclosure, the alert may comprise the video/still imagescombined with the analyte type, the concentration detected and atimestamp.

The single board computer 20A may interact with the GUI 600 in a similarmanner as described above. For example, a user may input into the GUI600 a frequency of activating the gas sensors (period) and readingaveraged from the gas sensors. Additionally, the single board computer20A may transmit the outputs from the gas sensors (and other sensorsincluding particle detector) to the GUI 600 for display. The singleboard computer 20A may also transmit the alert (with or withoutvideo/still images) to the GUI 600. The GUI 600 may display the outputfrom the sensors, the alert with determined concentration and thevideo/still images.

In other aspects of the disclosure, the camera system 700 may be omittedfrom the system 1A and the system 1A may be used to trigger and externalcamera system to record in the area where an event (high concentrationis determined). The external camera system may be part of a buildingssecurity system.

FIG. 9 illustrates another system 1B in accordance with aspects of thedisclosure. In system 1B, there are a plurality of sensor units 10_(1-N) (collectively referenced as “10”). Each sensor unit 10 is in aparticular area. For example, each sensor unit 10 may be located in/neara different room of a building. For example, the sensor unit 10 may bepositioned in the duct associated with a room and obtain the air flowfrom the room. Each sensor unit 10 maintains its own log regardingoutputs from the sensor array 12. Also, each sensor unit 10 (acting as aclient) transmits the outputs (as directed) to the processing system100A. The processing system 100A determines the concentration using amodel (which was trained, tested and deployed). The processing system100A may issue an alert to another system when the determinedconcentration is above a threshold. The other system may be a securitysystem for the building. The processing system 100A maintains a log inmemory of the outputs from the sensor array 12 from each sensor unit 10.Each sensor unit 10 is preset with the network address of the processingsystem 100A. In an aspect of the disclosure, the network address may beupdated, as needed.

As described above, each sensor unit 10 transmits the outputs from thesensor array 12 to the processing system 100A (as needed, e.g.,continuously or periodic). However, if a network connection fails, thesensor unit 10 may continue to collect the output from the sensor array12 and store the same. The sensor unit 10 will repeatedly attempt totransmit the output and when connected, transmit output not previouslytransmitted. In this aspect of the disclosure, each sensor unit 10 mayhave a transmission flag indicating prior transmission (or not).

FIG. 10 is a flow chart illustrating a method for deploying a model(s)for determining concentration and type of analyte in accordance withaspects of the disclosure. At S1000, the dataset used for training andtesting a plurality of models are obtained. The dataset used fortraining and testing may be different depending on whether the modelsare for identification of the type of analyte or for both identificationand for determining a concentration. For example, in a case where themodel is only for identification of the type of the chemical (and notfor concentration), the dataset for each chemical and combinations ofchemicals may only have sensor patterns for two points: a baseline whereno chemical is in a test chamber and a second point where a specificamount of the chemical or combinations of chemicals are placed in thetest chamber. On the other hand, in a case where the models are fordetermining both the type and the concentration, the dataset for eachchemical and combinations of chemicals may have more points: a baselinewhere no chemical(s) are in the test chamber and a plurality of pointsat different known liquid volumes. The known liquid volumes may beconverted into a concentration such as parts-per-million or a percentageusing a vapor pressure and volume of the test chamber. The volume of thetest chamber is known in advance.

The training/testing may be performed on different common solvents suchas isopropanol, ethanol, methanol, acetone, etc . . . The chemicals (andcombinations thereof) used in the training and testing may beapplication specific. For example, where the system is deployed in achemical storage cabinet, the training and testing may be done for eachchemical and combination of chemicals in the chemical storage cabinet.The system (or just the sensor unit 10) may be positioned in ahood/exhaust or within the test chamber. Temperature and pressuresensors may be deployed in the environment for calibration andconversion. Vapor pressure is temperature dependent.

The acquisition time for each point may be predetermined, such as butnot limited to 5 minutes. Each sensor output may be averaged. For thebaseline measurement, no chemical was placed in or near the testchamber. The output from each sensor in the sensor array 12 may beaveraged and recorded.

Afterwards, fixed amount of a first chemical may be injected onto apetri dish in the test chamber. This fixed amount of the first chemicalmay be where the vapor pressure in the syringe pump is saturated. Thesaturated vapor pressure is different for different analytes.

The fixed amount may be 100 ml. Other fixed amounts may be used. In acase where the training/testing is for a concentration, the vaporpressure and volume of the test chamber is used to determine thepercentage or ppm associated with the liquid volume.

Information about the first chemical may be recorded into the system1/1A/1B. The information may include information from NIOSH OSHAdatabases such as recommended exposure limits, lethal dose, immediatedanger limits, flash point, autoignition temperature, explosive limits,coefficients for Antoine equation for calculating the vapor pressure forthe chemical. This information may be used to determine the thresholdsfor alerts and autogeneration of instructions to responders withinformation on safety protocols related to said response.

The sensor array 12 may be exposed to the analyte (first chemical) forthe same period of time, e.g., 5 minutes, and the outputs from each gassensor may be averaged and recorded.

This process may be repeated for each single chemical used in thetraining/testing. However, between each data point, the sensor array 12may be equilibrated to atmospheric conditions (baseline) until theresponse is stable. Once the data is collected for each single chemical,data may be collected for all potential combinations of the chemicals.For example, if there are four chemicals used for training and testing:A, B, C, D, the data may be collected for A, B, C, D, AB, AC, AD, ABC,ABD, BC, BD, BCD, CD, ACD, ABCD. Where combinations are used, theliquids of the different chemicals, may be separately injected intoseparate petri dishes.

When the training and testing is to deploy a model for concentration, inaddition to obtaining the sensor output for the baseline and the fixedamount described above, data is acquired for multiple liquid volumes inbetween. For example, a calibration line may be created for each sensorin the sensor array 12. The calibration line may be a linear linebetween the baseline and the sensor output for the fixed amountdescribed above. Another data point may be acquired between the baselineand the fixed amount (such as mid-way). For example, 50 ml of the firstchemical may be used. Using the calibration line, an estimated sensoroutput may be determined. The sensor array 12 is exposed to the chemical(first chemical) for the same period of time, e.g., 5 minutes (afterequilibrium), using the 50 ml injected into the petri dish in the testchamber (third point) and the outputs from each gas sensor may beaveraged and recorded. Once again, the liquid volume may be convertedinto a percentage or ppm (concentration).

The difference between the measured output and the expected output maybe determined. When there is a difference, it means that the responsemay be non-linear. When the response is non-linear, additional volumesof the chemical near the previous volume may be obtained fortraining/testing. In an aspect of the disclosure, when the error is lessthan the background, additional data points may not be further acquired,e.g., enough data has been acquired for training and testing models.

The calibration lines for each sensor may be updated with the measuredoutput from the respective gas sensor. The calibration line may now benon-linear (curve). Additional data may be obtained in a similar mannerfor each single chemical, e.g., identify a new volume of the chemical,estimate the gas sensor(s) response, obtain the actual output anddetermine the distance. The new volume of the chemical fortraining/testing may be half of the previous amount. Additionally, asnoted above, the new volumes may be based on the magnitude of thedifference between the estimate response and the actual response. Whenthe magnitude is larger than the difference from other estimated/actualresponses, the next liquid volume may be closer to liquid volume withthe larger difference.

The above process may be repeated for each single chemical, until thedifference is less than a target amount.

The calibration lines (curves) for each sensor in the array 12 may beused to determine which sensors show the highest sensitivity to thechemical.

Once data points are collected for each single chemical (separately) atdifferent liquid volumes, data points may be collected for differentchemical combinations (at different combinations of liquid volumes). Forexample, when there are two chemicals (A and B), the liquid volume of Amay be maintained at a specific volume and the liquid volume of Bchanges. Afterwards, the liquid volume of B may be maintained at aspecific volume and the liquid volume of A is changed.

After the dataset is acquired (all of the data points are recorded), theprocessor 102 or single board computer 20A, splits the data into atraining set and a testing set at S1005. In an aspect of the disclosure,the processor 102 or single board computer 20A uses 5-fold randomcross-validation to split the dataset. For example, the model testingmay be accomplished using the 5-fold cross-validation, where X % of thedataset is randomly selected to be used for training, and the remainingY % of the dataset is used for testing. This process is performed foreach model type and each combination of hyperparameters, and repeated 5times so that a different training dataset is selected each time.

A S1010, a plurality of machine learning (ML) models may be trainedusing different combinations of hyperparameters using the training setsplit in S1005. The plurality of ML models includes models fromdifferent ML techniques include random forest, neural networks andsupport vector regression algorithms. The sets of hyperparameters may berandomly selected. For example, the hyperparameters for random forestinclude number of trees in the forest and depth of each tree. Thehyperparameters for a neural network include number of hidden layers,number of nodes within each hidden layer, and an optimizer. Thehyperparameters for the support vector regression algorithm includeKernel, C, and epsilon. The number of ML models trained (and tested) maybe application specific or a user parameter. For example, 10000different models/given hyperparameter sets may be trained. FIG. 10 showsan example of the bounds of the hyperparameters that may be used in thetraining. Different combinations of the gas sensor outputs may beevaluated in the training. For example, ratios of the gas sensor outputsmay be used in the training. In addition, the individual outputs werealso used as inputs to the ML models.

In some aspects, only the gas sensor outputs that had a high sensitivitymay be used in the training. In some aspects of the disclosure, modelsmay be trained to reach a predetermined percentage accuracy. Forexample, the predetermined percentage may be 95%. In some aspects, wherethe model is for both type and concentration, the model may be traineduntil the accuracy of both exceed 95%. In other aspects, differentmodels may be trained for the type and concentration. If a model doesnot exceed the predetermined percentage, additional data may be acquiredfor different combinations of concentrations or types (more datapoints).

At S1015, the trained models may be tested using the testing set fromS1005 (data from each cross-validation split). In an aspect of thedisclosure, the performance of each model may be determined using aparameter such as root mean square error (RMSE). RMSE was determinedbased on the actual type/concentration and the predicted type andconcentration using each model.

At S1020, the processor 102 or single board computer 20A selected thehighest performing model from among the plurality of ML models trainedand tested. For example, the processor 102 or single board computer 20A,compares the RMSE from each model and selects the model with thesmallest RMSE. This model is subsequently used for sensing at S1025. Forexample, the model with a given hyperparameter configuration whichperforms the best at predicting the testing dataset on average over allthe cross-validation splits may be selected to be deployed. The selectedmodel is stored in memory S1025.

In an aspect of the disclosure, different models may be selected for theidentification of the type and concentration for different chemicals orcombinations. For example, one model may have the best RMSE forconcentrations from Benzene and Ethanol while another model may have thebest RMSE for isopropanol and acetone. Therefore, in accordance withaspects of the disclosure, different models may be used depending on theapplication. Furthermore. one model may have the best RMSE foridentifying a type of chemical and a second model may be the best RMSEfor determining concentration.

FIG. 11 is a flow chart illustrating a method in accordance with aspectsof the disclosure. In this aspect of the disclosure, the method isexecuted by the processing system 100/100A. At S1100, the processor 102receives the outputs from the gas sensors from the sensor unit(s) 10(such as via wireless communication). In some aspects of the disclosure,the outputs may be an average of a plurality of consecutive outputs(digital values) in time. The received data may be contained in acommunication packet. The packet may have a header indicating the sourceof the packet (and destination). The received output from the gassensors may also include an identifier identifying the specific gassensor associated with the output.

At S1105, the processor 102 determines the concentrations of theanalytes (and type) using the deployed model(s). In an aspect of thedisclosure, the processor 102 retrieves the deployed models from thememory 104. In a case where there are multiple sensor units 10, theremay be different deployed models for different sensor units. Asdescribed above, different models may be customized for differentapplications (chemical groups). For example, a building may havemultiple rooms and each room may store or have different chemical(s) orcombinations of chemicals. The different chemical(s) or combinations maylead to a different model deployed based on the deployment criterion.Therefore, the processor 102 may retrieve the deployed model(s) for thespecific sensor unit 10 (based on the identifier in the source in theheader).

Based on the deployed model(s) and the received gas sensor output(pattern), the processor 102 may calculate the parameters used in thedeployed model(s). For example, if the deployed model relies on one ormore ratios of the gas sensor outputs, the processor 102 calculates theone or more ratios. Also, if the deployed model relies on amultiplication, addition, subtraction (weighted or unweighted), theprocessor 102 makes the appropriate calculation(s) needed for the model.After making the appropriate calculations, the processor 102 applies themodel(s) to the calculated values to obtain the type(s) (classification)and concentration(s).

At S1110, the processor 102 stores the determinations and a time stampin the memory 104 as an entry. For example, the processor 102 stores thedetermined type(s) and concentration(s) with the time stamp in thememory 104 in a CSV format.

At S1115, the processor 102 determines whether an event has occurred.For example, the processor 102 may determine whether a chemical has beenspilled in a room (or there is a leakage in a gas line or a storagebottle was not fully closed). In an aspect of the disclosure, thisdetermination may be based on a comparison with a threshold. Thethreshold may be stored in the memory 104 as described above. In someaspects, different thresholds may be used for different chemicals(analytes). For example, chemicals that are more dangerous or harmful,may have a lower threshold. In some aspects, different thresholds may beused for the same chemical, e.g., recommended exposure limit, lethaldose, immediate danger limit, explosive limits etc.

For each type and concentration determined, the processor 102 maycompare the concentration determined with the concentration threshold.When the processor 102 determines that the determined concentrationexceeds the concentration threshold (“Y” at S1115), the processor 102may issue an alert to another system such as a security system of thebuilding at S1120. As described above, the alert may include the typeand concentration detected (and the location). Different thresholds mayhave different warnings.

In other aspects, instead of using a preset concentration threshold, theprocessor 102 may determine that an event has occurred by comparingconsecutive determined concentrations. Since the processor 102 storesthe determined types and concentrations with a time stamp, the processor102 may calculate a change in concentration for a particular chemical(analyte). The processor 102 may determine that an event has occurredwhen there is a change in concentration for the type between theconsecutive times. In other aspects, the determination may be based onwhether the change is higher than a threshold. In other aspects, thedetermination may use multiple consecutive concentration determinationsand calculate a derivative or second derivative of the change.

The above method may be repeated for each sensor unit 10 in the system1B (if there are multiple sensor units. In the case of multiple sensorunits 10, the processor 102 may determine the location of the eventbased on the identifier of the sensor unit 10. Multiple sensor units 10may be in the same room and the location of the event within the sameroom may also be identified based on the responses from each sensor unit10 (e.g., different hoods in the same room).

The above method was described with respect to the processing system100/100A (and processor therein) executing the features, however, inother aspects of the disclosure, as described with respect to FIGS. 7and 8, there does not need to be a separate processing system remote(client/server configuration) and the single board computer 20A controlsthe gas sensors (causes power to be suppled, obtains the outputs,trains, tests, deploys the model(s) and determines the type(s) andconcentration(s).

FIG. 12 is a flow chart illustrating another method in accordance withaspects of the disclosure. In this aspect of the disclosure, the methodis executed by the processing system 100/100A. In this method, when anevent is detected at S1115, the processor 102 may cause a camera systemto activate at S1200. The camera system may be a standalone camera in aroom or a camera which is part of a security system of a building.

In an aspect of the disclosure, where the single board computer 20A isexecuting the features in FIG. 12, the single board computer 20A mayactivate the local camera system 700 via a switch 19. In an aspect ofthe disclosure, the single board computer 20A may superposed a warningor indicator on the video or still images obtained from the local camerasystem 700. Additionally, the combined video/image with the warning maybe transmitted to the security system.

Since the system 1A may be portable or positioned on a base, the system1A may also be used for detecting abnormal compositions (type andconcentrations) in a person's breathing. In this aspect of thedisclosure, the system 1A may be trained and tested by obtaining adataset of a person breathing near the system 1A (air channel 800)(person specific training). An event may be determined when there is achange in the compositions over time. In this aspect of the disclosure,the system 1A may transmit an alert to (1) the person; and (2)healthcare provider (doctor). In an aspect of the disclosure, thecontact information for persons such as phone number, email addressetc., may be registered into the memory in advance and the alert may betransmitted using the contact information.

In an aspect of the disclosure, the alerts may be transmitted based onthe magnitude of change or concentrations. For example, a slight changemay be alerted to a person whereas a large change or high concentrationmay be alerted to the healthcare provider.

Additive Manufacturing

In other aspects of the disclosure, the sensor systems described herein(such as the system 1A depicted in FIG. 7) may be used in an additivemanufacturing system 1350. However, in some aspects, the camera system700 may be omitted. The sensor system(s) may be used to determinewhether there is a defect in a product made using an additivemanufacturing process. Additive manufacturing makes three-dimensionalobjects from one or more materials. The materials may include polymers,metals and composite materials.

The polymer may be a thermoplastic resin, thermosetting resin or anelastomer. The thermoplastic resin may be polystyrene, polycarbonate,acrylic resin, etc. . . . The thermosetting resin may be epoxy,polyurethane, polyester, polyimide, polydimethylsiloxane (PDMS). Theelastomer may be ethylene-propylene rubber, a polybutadiene rubber, astyrene-butadiene rubber, a chloroprene rubber, or astyrene-butadiene-styrene block copolymer. The product or object maycontain more than one polymer.

The material(s) may be heated to certain temperatures using a flow rate(speed in which the materials are feed to the extruder head 1300) andmanufactured on a head bed 1305. In some aspects, the extruder head 1300may have a laser (such as for metal powder consolidation). A controller1320 controls the temperature of the extruder head 1300 (heater or laserpower) and the flow rate of the materials from the hopper(s) 1315.Decomposition or changing the structure of material(s) leads toformation of defects in the manufactured object. “Object” and “Product”are used interchangeably herein. These defects, depending on the area(s)they occur may lead to the failure of the object in operation or use.However, during the additive manufacturing, decomposition may beunpredictable and randomly occurring.

In accordance with aspects of the disclosure, the gas sensors in thesensor array 12 provides information on the gas phases released from thematerial(s) used in the additive manufacturing process. For example,during metal additive manufacturing, the gas phase may contain metaloxide (partial or fully oxidized), particles and by-products of reactionof metal vapor with inert gas(es) or other gas(es). The gas phase inpolymer additive manufacturing may include polymer and informationregarding degradation of products. As such, in accordance with aspectsof the disclosure, the machine learning model(s) may be trained/testedand deployed to detect signatures indicating a change in quality of gasphases (environment around the extruder head 1300) to determine a defectin the manufacturing process and may stop printing, as needed.

In accordance with aspects of the disclosure, one or more sensor systems1A may be mounted to the extruder head 1300. In an aspect of thedisclosure, the systems 1A may be mounted using a mounting bracket. Inan aspect of the disclosure, this same mounting bracket may be used tomount the extruder head to the remaining parts of the additivemanufacturing device. The description herein refers to system 1A howeverother of the described systems may be used. For example, one sensorsystem 1A may be mounted on the left side of the extruder head 1300 andanother sensor system 1A may be mounted on the right side of theextruder head 1300. Since the extruder head 1300 may move both rightwardand leftward in the additive manufacturing process, the gas phase may bereceived by the air channel in one of the systems 1A irrespective of thedirection of movement of the head 1300. In some aspects of thedisclosure, one or more fans 1310 may be used to direct the airflow tothe air channel in the system 1A. In some aspects, since the gas sensorsmay be temperature sensitive, the housing 850 of the system 1A may bemounted via an insulator to thermally isolate the gas sensors from theheat used in the additive manufacturing process. For example, thermaltape may be used.

In an aspect of the disclosure, the controller 1320 may be incommunication with the system 1A. The communication may be wireless. Thecontroller 1320 may transmit the manufacturing temperature (laserpower), flow rate and coordinate of the printing (x, y, z) to the system1A (to the single board computer 20A). In other aspects, the sameprocessor (controller) may be used to control all aspects of theadditive manufacturing process and defect determination in accordancewith aspects of the disclosure.

In an aspect of the disclosure, the model for determining a defect inthe product may be trained, tested and deployed in a similar method asdescribed above in FIG. 10. However, the dataset used for training andtesting may be acquired differently.

The dataset for training and testing of a plurality of models forpolymers and composite materials may be obtained heating the same to aplurality of different temperatures (and using a plurality of differentflow rates). In an aspect of the disclosure, to avoid any damage to theextruder head 1300, the dataset may be obtained using a hot platepositioned below the system 1A (gas sensor array 12). The hot plate maybe positioned within the housing of the additive manufacturing device,such as a printer. The hot plate may be controlled to heat a polymer andcomposite material to a desired temperature(s). For example, as abaseline value, the temperature may be the glass transition temperatureof the material. Different polymers have different glass transitiontemperatures and thus may have a different baseline. The dataset may begenerated for multiple different temperatures above the glass transitiontemperature. The highest temperature in the temperature range foracquisition of the dataset may be +100 C above the glass transitiontemperature. The highest temperature may be set based on the expectedadditive manufacturing temperature (even under an abnormal condition).For example, typically even under an abnormal condition, the additivemanufacturing temperature is unlikely to exceed the glass transitiontemperature by more than 100 C.

The first data point in the dataset may be obtained by heating a sampleof a particular polymer to the glass transition temperature Tg for thatparticular polymer. Tg is a known temperature for a polymer. The outputsof each of the gas sensors in the sensor array 12 may be recorded. Therecorded values may be an average of a plurality of consecutive sensorvalues. For example, the sensor readings may be taken over a 1 minuteperiod or 2 minute period. Additionally, the recording may be startedafter stabilization occurred in the temperature. Background readings(noise adjustment) may be used to normalize or account for typical gasesin the environment, e.g., obtain sensor output prior to placing thepolymer in the hot plate and heating.

Once the first data point is obtained, the second data point in thedataset may be obtained by heating a sample of the particular polymer toTg+100 (maximum) after waiting for the gas sensors in the sensor array12 to return to the output without any heating or gas sensing (e.g.,background normal values). The sensor output of each sensor in thesensor array 12 may be averaged and recorded. A calibration line may becreated for each gas sensor in the sensor array 12. The calibration linemay be a linear line between the baseline (Tg) and the gas sensor outputfor the Tg+100 C described above. Another data point may be acquiredbetween the baseline (Tg) and the Tg+100 C (such as Tg+50C). Using thecalibration line, an estimated sensor output may be determined. A sampleof the polymer may be heated to Tg+50 and the sensor array 12 is exposedfor the same period of time, e.g., 1 or 2 minutes (after equilibrium),and the outputs from each sensor may be averaged and recorded.

The difference between the measured output and the expected output maybe determined. When there is a difference, it means that the responsemay be non-linear. When the response is non-linear, additional data fromheating temperatures near the previous temperature may be obtained fortraining/testing.

The calibration lines for each sensor may be updated with the measuredoutput from the respective sensor. The calibration line may now benon-linear (curve). Additional data may be obtained in a similar mannerfor each heating temperature of the same polymer, e.g., estimate thesensor(s) response, obtain the actual output and determine the distance.In some aspect, the new heating temperature for training/testing may behalf of the previous amount. Additionally, as noted above, the newheating temperature may be based on the magnitude of the differencebetween the estimate response and the actual response. When themagnitude is larger than the difference from other estimated versesactual response, the next heating temperature may be closer totemperature with the larger difference.

The above process may be repeated until the difference is less than atarget amount.

In other aspects, the dataset may be obtained starting from Tg andheating a sample in increments of 10 degrees steps until a maximum isreached (Tg+100 C). In other aspects, the dataset may be obtainedstarting from Tg and heating a sample in increments of 1-degree stepsuntil a maximum is reached (Tg+100 C). In this aspect, the dataset mayhave 10-100 different patterns of sensor outputs from the sensor array12. In other aspects, the temperature may be maintained, but the flowrate changed to obtain data points at different flow rates.

The above process may be repeated for each polymer or composite materialexpected to be used in the additive manufacturing process. In otheraspects of the disclosure, when other types of additive manufacturingprocesses are used (such as using a laser), the dataset may be obtainedfor different laser powers instead of different heating temperatures.

An abnormal condition may be detected during the manufacturing when theactual sensor response corresponds to a predicted temperature (from thedeployed model) higher than the temperature received from the controller1320 (target temperature used).

In other aspects of the disclosure, the dataset may include measurementsand data from different modalities of measurements. The differentmodalities may also measure the gas phase or solid phase such as massspectroscopy, Fourier-transform infrared spectroscopy, thermalgravimetric analysis, and Raman spectroscopy. These modalities ofmeasurements may be conducted simultaneously with the gas sensing viathe sensor array 12. For example, a FT-IR spectrometer from BrukerVERTEX series may be used for the FTIR spectroscopy. A ThermogravimetricAnalyzer available from TA Instruments such as Discovery TGA 55, TGA 550or TGA 5500 may be used for the thermal gravimetric analysis. An InViaconfocal Ramon microscopy may be used for the Raman spectroscopy. Whilethese modalities of measurements may be used for training/testing anddeployment of a model (correlation), once the model is deployed, onlythe gas sensor output from the sensor array 12 may be used as the inputfor predicting the temperature and decomposition as the other modalitiesmay be costly. These other modes of measurement may be used at eachtemperature in the dataset (acquisition temperature). The modes may beused for detection of decomposition of the product as a function oftemperature.

Additionally, mechanical testing/analysis may be obtained and includedin the dataset. The mechanical testing may be acquired from printing anobject using the extruder head 1300. While the dataset may include gassensing and spectroscopy measurements from Tg −Tg+100 C, the mechanicaltesting may only occur at a subset of the range, e.g., Tg −Tg+20C toavoid damaging the extruder head 1300. The mechanical testing mayinclude manufacturing one or more predetermined shaped objects using thepolymer (or composite material) (for each polymer/composite material) atdifferent temperatures.

The mechanical analysis at each temperature may include closeness totarget shape (warping), adhesion and strength such as Young's modulus.The mechanical analysis may identify key areas of weakness (in theproduct), e.g., locations, which may be critical to the overall designof the product. For example, when there is an abnormal printing within aproduct (not on the surface), the defect may not impact the shape suchas warping, however, when the abnormal printing is at the surface, itmay impact the shape. For example, by overheating a polymer, the surfacemay be curved and not be able to be formed with sharp angles (corners).This may be a critical defect and cause rejection of a product.Additionally, if the abnormal printing is on a base of a product, e.g.,weight bearing, and causes the strength of the object to be reduced, thebase may not be strong enough to hold the weight of other layers of theproduct, causing failure of the product.

In an aspect of the disclosure, the results of the mechanical analysismay be manually entered into the dataset for each temperature that theanalysis was performed. The entry may include the type of failure, e.g.,issue such as warping or strength and positioned of the failure andwhether the failure is a key or critical position and tolerances. Thisinformation may be used to determine whether to stop the additivemanufacturing process prior to finishing the product.

Once the dataset is generated for each polymer or composite material(and metals), the dataset may be divided into sets for training andtesting in a similar manner as described above.

In some aspects of the disclosure, different models may be deployed fordifferent polymers or composite materials. Therefore, the dataset forthe same polymer or composite material may be used to generate thetraining and testing sets.

In other aspects, the same model may be deployed for the differentpolymers and composite materials.

As described above, a plurality of machine learning (ML) models may betrained using different combinations of hyperparameters using thetraining set. The plurality of ML models includes models from differentML techniques include random forest, neural networks and support vectorregression algorithms. The sets of hyperparameters may be randomlyselected. For example, the hyperparameters for random forest includenumber of trees in the forest and depth of each tree. Thehyperparameters for a neural network include number of hidden layers,number of nodes within each hidden layer, and an optimizer, Thehyperparameters for the support vector regression algorithm includeKernel, C, and epsilon.

The number of ML models trained (and tested) may be application specificor a user parameter. For example, 10000 different models/hyperparametersets may be trained where FIG. 10 shows an example of the bounds of thehyperparameters that may be used.

Different combinations of the gas sensor outputs may be evaluated in thetraining. For example, ratios of the gas sensor outputs may be used inthe training. In addition, the individual outputs of the sensors in thesensor array 12 were also inputs to the models.

In some aspects, only the sensor outputs that had a high sensitivity maybe used in the training. In some aspects of the disclosure, models maybe trained to reach a predetermined percentage accuracy. For example,the predetermined percentage may be 95%. If a model does not exceed thepredetermined percentage, additional data may be acquired for differenttemperatures.

The trained models may be tested using the testing set (data from eachcross-validation split). In an aspect of the disclosure, the performanceof each model may be determined using a parameter such as root meansquare error (RMSE). RMSE was determined based on the target temperatureand the predicted temperature using the model.

The highest performing model from among the plurality of ML modelstrained and tested is selected for deployment. For example, the singleboard computer 20A, compares the RMSE from each model and selects themodel with the smallest RMSE. This model is subsequently used forsensing. The selected model is stored in memory.

In accordance with aspects of the disclosure, the single board computer20A may maintain a table 1400 of information associated with theadditive manufacturing process of a product such as shown in FIG. 14.The information in the table 1400 may be used to generate a report(digital passport) for the product. The report may be generated for eachproduct or object manufactured.

In an aspect of the disclosure, the table 1400 may include informationreceived from the controller 1320 such as the x, y, z position of theextruder head 1300 (pixel being printed), printing conditions (such astemperature or laser power, a flow rate, type of material, e.g., whatthe polymer, metal or composite material is), time the printing started,and other information such as manufacturer of the material, batch numberof the material, etc. The table 1400 may also include informationdetermined by the single board computer 20A such as the chemicalsignature (gas pattern), whether an abnormal condition has occurred(such as predicted temperature using model verses target temperature ofprinting is different), a timestamp of received gas sensor output andwhether printing (manufacturing) of the product is stopped.

The report (digital passport) may be used for post-failure analysis (ifthe manufacturing is allowed to continue) and the product ultimatelyfails in use. The post-failure analysis may include determining whetherthere is a design issue with the part or a one-time failure. In otheraspects of the disclosure, the reports (digital passports) may be usedto determine if there is an error or failure in the printer itself. Forexample, if multiple reports indicate an abnormality in themanufacturing process for multiple different products (of the same ordifferent design) within the period of time), this may indicate that theprinter needs to be recalibrated or repaired. Additionally, if thereports indicate an abnormality in the manufacturing process only forthe same product (in the same location), this may indicate a flaw in thedesign of the product.

FIG. 15 illustrates a method in accordance with aspects of thedisclosure. This method may be used to determine whether there is anabnormality in the manufacturing process and whether to stop themanufacturing process due to the abnormality.

In accordance with aspects of the disclosure, the single board computer20A may receive the output from each of the plurality of gas sensors inthe sensor array 12 (gas sensors) at S1500. The output may becontinuously received or periodically received. The period may bedetermined by a user via a GUI (similar to described above). The outputfrom each gas sensor may be averaged. The number of readings that areaveraged may be based on a user setting (also via the GUI).

The single board computer 20A may also receive the printing location andprinting conditions from the controller 1320 at S1505. The printinglocation may be in x, y, z coordinates. The printing conditions weredescribed above. S1500 and S1505 may occur at the same time. In anaspect of the disclosure, the printing conditions may be received priorto printing and the same condition is assumed for the entire process. Inother aspects, the printing conditions are continuously updated and sentto the single board computer 20A on a pixel-by-pixel basis.

At S1510, the single board computer 20A may retrieved the deployedmodel(s) for the material(s) used in the additive manufacturing process(such as the polymer(s), metals or composite material(s). Where the samemodel is used for all materials, the single board computer 20A retrievesthe one model. However, where different models are deployed based on thematerial(s), the single board computer 20A retrieves the modelassociated with the material(s) (identified in the printing conditions).

Based on the deployed model(s) and the received gas sensor output, thesingle board computer 20A may calculate the parameters used in thedeployed model(s). For example, if the deployed model relies on one ormore ratios of the gas sensor output, the single board computer 20Acalculates the one or more ratios. Also, if the deployed model relies ona multiplication, addition, subtraction (weighted or unweighted), thesingle board computer 20A makes the appropriate calculation(s) neededfor the model. After making the appropriate calculations, the processor102 applies the model(s) to the calculated values to obtain thetemperature and decomposition information based on the pattern of outputfrom the gas sensors at S1515. For example, the pattern may predict atemperature and decomposition level based on the training/testing andcorrelations described above. Since the single board computer 20Areceives the target temperature in the printing condition, if the modelpredicts a temperature that is different than the target temperature,the single board computer 20A may determine that an event has occurredat the pixel) (YES at S1520). While there may be a difference, the eventmay not indicate a decomposition in the material(s) (depending on thedifference and the temperature). In an aspect of the disclosure, thesingle board computer 20A may examine the decomposition level predictedusing the deployed model and the pattern of output from the gas sensors.If the decomposition level is greater than a threshold, the single boardcomputer 20A may determine that there is an event at the pixel (YES atS1520). Otherwise, the single board computer 20A may determine thatthere is no event at the pixel (NO at S1520). When there is nodifference in temperature (predicted and actual), the single boardcomputer 20A may determine that no event has occurred at the pixel (NOat S1520).

When an event has been determined, the single board computer 20A maydetermine whether the event is at a key location (critical location) atS1525. This determination may be based on user defined informationentered as part of the mechanical analysis described above. The pixelbeing manufactured is determined from the printing conditions receivedfrom the controller 1320. The pixel location is compared with keylocations. When they coincide (YES at S1525), the single board computer20A may transmit an instruction to the controller 1320 to stop themanufacturing process for the product at 51530.

The table 1400 for the pixel is updated with the determinations atS1535. For example, the table 1400 for the pixel is updated to includewhether the printing is stopped or not, the predicted decompositionlevel and temperature (may be included in the chemical signature). In anaspect of the disclosure, the pattern of gas sensor outputs (outputsfrom the gas sensors may also be stored in the pixel record in thechemical signature (at S1500) and the printing conditions and otherinformation) may be stored at S1505. When the manufacturing of theproduct is stopped, the product may be labelled as defective andrecycled for reuse in other manufacturing processes.

The table 1400 may be used to generate a report. This report may betransmitted to the controller 1320 via a communication interface (wiredor wireless).

Sensing Age/Quality of Food or Beverage

In other aspects of the disclosure, a sensor system described hereinsimilar to one described in FIG. 8 may be used to predict the age of afood or beverage and determine whether the quality of the food orbeverage has deteriorated such as being spoiled. FIG. 16 illustrates anexample of a sensor system 1AA in accordance with aspects of thedisclosure. The sensor system 1AA may be a portable system such ashandheld system. The system 1AA may have a handle (not shown). In otheraspects of the disclosure, the sensor system 1AA may be mounted on thewall of a refrigerator. For example, the housing 850A may have a magnetsuch that the housing 850A may be attached to a magnetic surface of therefrigerator (door or wall). In other aspects, the sensor system 1AA mayhave a base or stand (not shown) such that the sensor system 1AA may beplaced on a kitchen counter or on a shelf of the refrigerator. The standor base may be hollow in order for the air to flow into the opening inthe bottom of the housing 850 (and enter the air channel 800). The standor base raises the bottom surface of the housing 850 to a distance abovethe counter or shelf.

In some aspects, the system 1AA may comprise fans for forcing air toflow through the air channel 800. However, given the configuration ofthe air channel 800 and gas sensors, the heat from the sensors causesmovement via convection and therefore, fans may not be needed to have aconstant flow.

The sensor system 1AA may have a display such as a touch panel 1600. Thetouch panel 1600 may receive an identification of a food or beverage forage and quality detection. In an aspect of the disclosure, the touchpanel 1600 may have buttons in certain areas of the panel correspondingto foods or beverages. For example, one touch button may be for milk,another for food, such as beef and salmon. In other aspects of thedisclosure, the touch panel 1600 may display a list of foods. Forexample, the list may include apples, bananas, pears, duck, etc. . . .In some aspects of the disclosure, the touch panel 1600 may display onlyfood or beverage items that the sensor system 1AA have been trained andtested on. In another aspect of the disclosure, the touch panel 1600 mayenable a user to manually enter the type of food or beverage by spellingout the name of the item.

In other aspects of the disclosure, instead of or in addition to thetouch panel, the sensor system 1AA may comprise an identificationscanner/camera system 700A. The identification scanner/camera system700A may be configured to scan an identification code on the food orbeverage such as on the package, such as a bar code scanner, a QR codescanner or a UPC code scanner. The identification code may convey to thesensor system 1AA the type of food or beverage and other manufacturinginformation including the recommended expiration date and packaged date.

In other aspects, the camera system 700A (image processor) may recognizethe image of the food or beverage itself without a need for a package.

In an aspect of the disclosure, the sensor system 1AA may be trained andtested using a plurality of machine learning models to deploy a modelfor determining the age and quality of a food or beverage. Differentmodels may be deployed for different types of foods. Additionally,different models may be deployed for different sub-types, brands orkinds within the type. For example, a different model may be deployedfor red apples verse green apples. A different model may be deployed fordifferent red apples. Different models may be deployed for differentkinds of fishes or meats. For example, different models may be deployedfor ground beef, rib-eye steaks or skirt steaks.

Model(s) may be deployed in a similar manner as described above in FIG.10. In order to train/test the plurality of models (and subsequentlydetermine which to deploy for a particular type or sub-type), a datasetfor training and testing is obtained.

In an aspect of the disclosure, each item of food or beverage may beseparately trained/tested.

An untrained/untested food or beverage item may be trained/tested byeither scanning the item (package) as described above, the type enteredvia the touch panel 1600 or the type recognized by image processing ofan image of the food or beverage acquired by the camera system 700A. Inan aspect of the disclosure, the sensor system 1AA may have a wired orwireless communication interface and search the Internet to recognizethe image of the food or beverage (via the single board computer 20A)acquired by the camera system 700A. Since this is the first time thefood or beverage is scanned, imaged or entered into the sensor system1AA, the sensor system 1AA will recognize that a model has not beencreated for the item and may enter a training/testing mode. The singleboard computer 20A may initiate a record in the memory for the food orbeverage item. The record may include a food index and food name. Whenthe identification code of the food or beverage is read, the amount ofthe item, recommended expiration date, packaging date and otherinformation may also be recorded in the record. When there is no packet,the single board computer 20A may obtain information such as recommendedexpiration date, recommended storage temperature from the Internet, suchas, from the Food and Drug Administration (FDA). In an aspect of thedisclosure, even if the packet has an expiration date, the single boardcomputer 20A may obtain a recommended storage lifetime (expiration date)from the Internet, such as from the FDA.

The food or beverage item is subsequently exposed to the sensor array 12and the output of the gas sensors is obtained by the single boardcomputer 20A, averaged (if needed) and recorded with the time, e.g.,day. This pattern of gas sensor output is taken as a baseline for thefood (e.g., day 1). The system 1AA assumes that the first time the itemis sensed is a fresh food or beverage. In an aspect of the disclosure,the time of the day may also be recorded in addition to the date. Thefood or beverage item may be kept near the opening on the bottom of thehousing 850A for one or more minutes such that the gas sensors in thesensor array 12 reach equilibrium. The start of averaging of the sensoroutput may commence after the equilibrium period. The temperature andpressure in the area may also be recorded. This is because the gassensor output may be different in different temperatures/pressures suchas on a counter verse in a refrigerator. In accordance with this aspectof the disclosure, the sensor system 1AA may have a temperature sensorwhich is used for calibration of the sensor array 12.

Additional data points may be subsequently acquired at differentdates/times. The frequency of acquiring the data points may depend onthe type of food or beverage, and how quickly the item deteriorates andspoils. For example, for a food or beverage item with a long shelf(storage lifetime) or an expiration date in the far into the future,fewer frequent data points may be needed than for a food or beverageitem with a short shelf (storage lifetime) such as fruits, meats orfish. For longer shelf (storage lifetime) items, data points may beobtained once a week. However, for shorter shelf (storage lifetimes) oritems expiring quickly, data points may be obtained daily or even twicea day. In some aspects, the frequency may not be the same over the lifeof the item (food or beverage). For example, initially, data points maybe obtained once a week, however, as the item nears is expiration dateor storage lifetime, data points may be acquired more frequently (suchas daily or twice a day). Additionally, even though an item has past its“expiration date” or “storage lifetime” it does not mean the food orbeverage is “spoiled” or deteriorated. Therefore, in some aspects, datapoints may be acquired even after the expiration date or storagelifetime. The frequency of acquiring the data points after theexpiration date or recommend storage lifetime may be even more frequencysince there is a higher likelihood that the food or beverage item hasspoiled or deteriorated.

At each data points, since different single food or beverage items maybe trained/tested simultaneously, the food or beverage item may bescanned/imaged/input such that the single board computer 20A recognizesthe item and adds the sensed pattern (and date) to the correct record.When all desired data points for a specific food or beverage item areacquired, the user may press a button or indicate finished.

Once all the data points are acquired for a particle food or beverageitem (and correlated with a date), the single board computer 20A dividesthe dataset collected into datasets for training and testing asdescribed above.

Also as described above, a plurality of machine learning (ML) models maybe trained using different combinations of hyperparameters using thetraining set. The plurality of ML models includes models from differentML techniques including random forest, neural networks and supportvector regression algorithms. The sets of hyperparameters may berandomly selected. For example, the hyperparameters for random forestinclude number of trees in the forest and depth of each tree. Thehyperparameters for a neural network include number of hidden layers,number of nodes within each hidden layer, and an optimizer, Thehyperparameters for the support vector regression algorithm includeKernel, C, and epsilon.

The number of plurality of ML models trained (and tested) may beapplication specific or a user parameter. For example, 10000 differentmodels/hyperparameter sets may be trained where FIG. 10 shows an exampleof the bounds of the hyperparameters that may be used.

Different combinations of the sensor outputs (patterns) may be evaluatedin the training. For example, ratios of the sensor outputs may be usedin the training. In addition, the individual outputs of the gas sensorsin the sensor array 12 may also be inputs to the models.

In some aspects, only the sensor outputs that have a high sensitivitymay be used in the training. In some aspects of the disclosure, modelsmay be trained to reach a predetermined percentage accuracy. Forexample, the predetermined percentage may be 95%. If a model does notexceed the predetermined percentage, additional data may be acquired fordifferent days and or amounts.

The trained models may be tested using the testing set (data from eachcross-validation split). In an aspect of the disclosure, the performanceof each model may be determined using a parameter such as root meansquare error (RMSE). RMSE may be determined based on the actual age ofthe food or beverage item verses the predicted age of the food orbeverage item using the model.

The highest performing model from among the plurality of ML modelstrained and tested may be selected for deployment. For example, thesingle board computer 20A compares the RMSE from each model and selectsthe model with the smallest RMSE. This model is subsequently used forsensing. The selected model is stored in memory.

During the training/testing, a specific pattern may be assigned oridentified as corresponding to the expiration date. Additionally,thresholds such as specific sensor patterns or a specific age may beadded to the model or correlated to the model such that if the sensorpattern output by the gas sensors in the sensor array 12 are predictedto be the specific age or later, the food or beverage item may be deemedto be spoiled. The specific age may be determined based on tasting ofthe item. Additionally, the specific age may also be based on uservisual inspection. In other aspects, the specific age may be based oninformation from the FDA. In an aspect of the disclosure, a percentspoilage may be determined based on the specific age or specific sensorpattern. For example, a new item may have a zero (0%) spoilage and anexpired item may have 100% spoilage. The percent spoilage may be linearinterpolated based on the specific sensor patterns detected. In otheraspects, a non-linear interpolation may be used. In other aspects, acombination of non-linear interpolation and linear interpolation may beused where an average of the non-linear and linear interpolation may beused for the predicted spoilage percentage.

The above process may be repeated for each food or beverage item desiredfor age detection.

In some aspects, a model may be deployed for a combination of food itemsor beverage items in a similar manner as described above. For example, asalad may contain multiple items such as lettuce, carrots, tomatoes,cucumbers, dressing, etc. . . . . The model for the combination of itemsmay be trained/tested and deployed in a similar manner as describedabove (where the dataset is acquired over time starting with abaseline).

FIG. 17 illustrates a method in accordance with aspects of thedisclosure. This method may be used to predict whether an item of foodhas expired, is spoiled (even though the date is prior to the recommendexpiration date) and percentage of spoilage (and to enter atraining/testing mode).

At S1700, the sensor system 1AA receives an identification of a food orbeverage item. The identification may be received via the identificationscanner (UPC code, bar code, QR code) or image recognition from an imageacquired via the camera system 700A or via the touch panel 1600, asdescribed above. In response to the receipt of the identification, thesingle board computer 20A may determine whether a model has beendeployed for the item. The single board computer 20A checks the memoryfor the stored model associated with the food or beverage item, e.g.,for the type by name. When the single board computer 20A determines thata model for the food or beverage item has not been deployed (NO atS1705), the single board computer 20A may determine if the datasetcollection process has already started, e.g., in the middle and notcompleted at S1710. The single board computer 20A checks if a record forthe food items has been opened in the memory and whether a data pointhaving an associated time and sensor pattern has been stored. When thesingle board computer 20A determines that a record is opened and a datapoint is stored (but not competed) (YES at S1710), the single boardcomputer 20A subsequently receives the output from the sensor array (andaverages) at S1715 and stores the pattern as a data point in the datasetat S1720. The single board computer 20A may display a screen asking ifthe dataset for the item is compete. When the dataset for the item iscomplete, the single board computer 20A divides the dataset is describedabove for training/testing.

When the single board computer 20A determines that a record is notopened and it is the first time the item is scanned (or identified tothe system 1AA), the single board computer 20A may display a screen onthe touch panel 1600 indicating that no machine learned model isdeployed for the item and request whether the user would like to startcreating the dataset for deploying a model at S1725.

When the single board computer 20A determines that a machine learnedmodel has been deployed for the food or beverage item (or combination offoods or beverages), the single board computer 20A retrieves the machinelearned model from the memory for the item and awaits the output fromthe sensor array 12. At S1715, the receives the output from the sensorarray (and averages).

At S1735, the single board computer 20A predicts the age of the food orbeverage item based on the output pattern from the sensor array 12 andthe deployed machine learning model. For example, based on the deployedmodel(s) and the received gas sensor output, the single board computer20A may calculate the parameters used in the deployed model(s). Forexample, if the deployed model relies on one or more ratios of the gassensor output, the single board computer 20A calculates the one or moreratios. Also, if the deployed model relies on a multiplication,addition, subtraction (weighted or unweighted), the single boardcomputer 20A makes the appropriate calculation(s) needed for the model.After making the appropriate calculations, the single board computer 20Aapplies the model(s) to the calculated values to obtain a predicted ageand quality based on the one or more combinations of output from the gassensors

At S1740, the single board computer 20A determines, when the predictedage coincides with the pattern associated with an expiration date (evenif the expiration date is not yet reached), that the item is expired.The single board computer 20A may also determine whether the currentdate is after the recommended expiration date stored in the record forthe item (from the FDA or from the package). If either determination isYES at S1740, the single board computer 20A may display a notificationin the form of a warning on the touch panel 1600 at S1745. In someaspects, the single board computer 20A may transmit a notification to apredetermined device, such as a mobile phone indicating that the producthas expired. In some aspects, the indication may distinguish whether thecurrent date is after the recommended expiration date (expiration date)or whether the aroma from the item indicates that the product has anaroma similar to the aroma at the expiration date (predicted date).

At S1750, the single board computer 20A may determine whether the foodor beverage item is spoiled. In an aspect of the disclosure, differentcombinations of sensor outputs (or a predicted age) may be correlated toa spoiled condition or a percentage of spoiled condition when the modelis deployed (the correlated may be entered into the dataset).

At S1750, the single board computer 20A may determine whether the sensoroutput acquired in response to the food or beverage item or predictedage using the sensor output as the input to the deployed model is sensorpattern or predicted age correlated to a spoiled condition (orpercentage) or is a predicted age older than an age that is correlatedto the spoiled condition (or percentage). When either of thesedeterminations is YES, the single board computer 20A may determine thatthe item is spoiled at S1750 (YES) and may display a differentnotification on the touch panel 1600 at S1745A. The differentnotification may be in a different color. In other aspects, thenotification may have words in CAP or BOLD or a danger symbol. When theitem is not spoiled (NO at S1750), the single board computer 20A maydisplay a different notification on the touch panel 1600 at S1745B. Thedifferent notification may be in a different color. For example, whenthe item is neither expired or spoiled, the display may be green, whenthe item is expired, the display may be yellow and when the item isspoiled, the display may be red. Similar to above, the notifications maybe transmitted to another device such as a mobile device. Contactinformation for the other device such as the mobile device may beregistered in the memory of the single board computer 20A.

In other aspects of the disclosure, instead of or in addition to, thesensor system 1AA may determine the age and quality of food or beverageitem based on analysis of images of the item. Similar totraining/testing/deployment using patterns of the sensed output from gassensors in an array 12, the training/testing/deployment may also be doneusing images of the item taken at different times. As foods andbeverages age, the color of the items may change. This discoloration maybe analyzed, and a model may be deployed based on the analysis.

In some aspects of the disclosure, the images used for training andtesting for an item may be obtained from the Internet. The single boardcomputer 20A based on an instruction to deploy a model for an item mayobtain multiple images from the Internet and descriptors of the images.The images may include a baseline (new food or beverage), expired foodor beverage and spoiled food or beverage. The images may be correlatedto the descriptors. In other aspects of the disclosure, the images usedfor training and testing may be acquired when the sensor patterns ofobtained from the gas sensors in the array 12. For example, when thatitem is held near the opening, the items may also be held in the line ofsight of the camera system 700A. In an aspect of the disclosure, thedeployed model may be generated using both the images of the item andthe outputs from the sensor array 12. As such, the determinations ofage, expired and spoiled may be based on both acquired current images ofthe item and the sensor outputs from the sensor array 12.

Natural Language Descriptor

In other aspects of the disclosure, a sensor system described hereinsimilar to one described in FIG. 8 may be used to predict a naturallanguage descriptor(s) associated with an item. The items may be foodsuch as fruits, vegetables, meats, fish, nuts, spices, herbs, dairyproducts and cereal. The items may be beverages such as alcoholicbeverages scotch, brandy, wine, whisky, beer, non-alcoholic beveragessuch as coffee, tea, sodas, juices, etc. The items may also be seeds,flowers, trees, etc.

A known current system approach is to assign natural languagedescriptors characterizing aroma to a particular chemical associatedwith the aroma. In the known system, the data may be processed usingprincipal component analysis, multivariable curve regression techniquesto define correlation between sensor signals and associated aroma. If atested aroma is within the boundaries of classified standard aromas itcan be identified as belonging to one or another class. One problem withthe current approach is the complexity of aromas comprising a mixture ofseveral components, which lead often times to incorrect identificationof the aromas.

In an aspect of the disclosure, the system 1A acquires the sensor outputand processing the same to predict complex aroma patterns without usingthe chemical structure, PCA or MCR analytics using a centroid approach.The aromas may be converted to the natural language descriptors usingindividual sensor output and/or ratios of the sensor outputs. In anaspect of the disclosure, a logistic regression model may be used topredict the natural language descriptors for the aroma and a confidence(percent confidence).

In an aspect of the disclosure, the logistic regression model may betrained and tested to predict the natural language descriptor(s) of theitem sensed. In this aspect of the disclosure, the sensor system 1A maya user interface such as a touch screen such that a user may enter atraining mode and input the natural language descriptor(s) and acoefficient for each during the acquisition of a dataset for trainingand testing. In an aspect of the disclosure, the camera system 700 maybe omitted. A matrix of samples may be created. The columns in thematrix may be the natural language descriptors and the rows may be thesamples. If aroma descriptor is not present in the sample, thecoefficient in the matrix is zero. If aroma is present in the same, thecoefficient of the cell in the matrix is larger than zero. If severalaroma descriptors are present, the coefficients of corresponding columnsare more than zero. The dominant descriptor is characterized by largercoefficient among other coefficients characterizing the sample. Theweakest aroma descriptor has the smallest non-zero coefficient amongother descriptors for the same sample. A sum of all aroma descriptorsfor a sample is equal to one, and individual descriptors correspond tothe fractional intensity of a particular aroma descriptor (zeroor >zero). The size of the matrix is the number of samples x the numberof aroma descriptors. If the item has a known natural languagedescriptor such as from a manufacturer or a flavor wheel, the user mayenter the natural language descriptors and coefficients based on theavailable information. However, if the item does not have a knownnatural language descriptor(s), an expert may smell the item and providethe natural language descriptor for the item and coefficient(s).

Each item for the dataset may be brought near the housing 850 and heldbelow the air channel to allow thermal convection from high temperatureof sensors to draft air with aroma into. In other aspects, a fan may beused to move the air into the air channel. For each sensor in the arraythe rise time of the signal as well as a value of stabilized signalresponse are measured for all samples. Therefore, each reference samplemay have an array of sensor responses and an array of sensor responserise time.

The name of the item may also be entered. The name may be used toconfirm that this is a new item for training/testing as opposed to thesame item being included having a different age.

In an aspect of the disclosure, another matrix may be generated. Thesize of this matrix may be number of samples x. number of sensors. In anaspect of the disclosure, additional matrixes may be generated withratio of sensor responses (and rise time). Each matrix may have adifferent sensor response ratio, e.g., S1/S2 and S2/S3.

The samples may be divided for training and testing and the modeltrained and tested using the respective dataset. Cross-validation may beused such that all of the samples may be used for training. In an aspectof the disclosure, the training is done until a preset accuracythreshold is reached. For example, the predetermined percentage may be95%. If a model does not exceed the predetermined percentage, additionaldata may be acquired for different samples. Further, in some aspects,only the sensor outputs that have a high sensitivity may be used in thetraining.

At least two items are used for training and testing. However, thelarger number of items used for training and testing, the better theprediction is of the natural language descriptor. There is a pluralityof natural language descriptors. For example, there may be 10descriptors of the aroma. In an aspect of the disclosure, there may bedifferent descriptors based on the type of the item. For example, thenatural language descriptors for coffees may be different from wines, orteas, which also may be different for hops.

In an aspect of the disclosure, when a percent remaining or percentdegradation (also referred to a percent depletion) is determinedadditional measurements are done for samples which are allowed todegrade their aroma (for instance samples are kept in the open air forday, two . . . week). For each day the sensor response is measured forall reference samples. In an aspect of the disclosure, the user mayinput the identifier or type of the sample and the day, e.g., day 2, day5, day 10 . . . etc . . . .

When all desired data points for a specific item are acquired, the usermay press a button or indicate finished.

As described above, both individual and different combinations of thesensor outputs (patterns) may be used in the model. For example, ratiosof the sensor outputs may be used in the training. In some aspects ofthe disclosure, two different gas sensor ratios may be obtained. In someaspects, three or more gas sensor ratios may be obtained.

Aromas associated with the same natural language descriptors may beclustered in three-dimensional space (centroids). FIG. 18 shows anexample of a cluster 1800 in three-dimensional space of a gas sensorpattern taken using three ratios of outputs, S1/S2, S1/S3 and S2/S3. Theoutputs may be projected into 2-D planes. Three projections are alsoshown in FIG. 18. The different clusters, e.g., different patterns, maybe used train the model to predict the natural language descriptors.

FIGS. 19A-19I illustrates an example of a dataset acquired from 9different hops over 18 days. FIG. 19A is an example of a scatter plot ofnormalized MQ-7 and MQ-5 sensor responses over 18 days of exposure tohops aromas. The size of points in the plot represents days (thesmallest points were measured on day 1 and the largest points weremeasured on day 18). FIG. 19A shows data from day 1, 2, 3, 4, and 18.FIG. 19A shows the nine different hops with clustered responses for thenormalized sensor responses. FIG. 19B shows the same scatter plot,however, it is identified using the natural language descriptors. FIG.19C shows centroids of each natural language descriptor. In FIG. 19Cthere is an error bar showing a standard deviation. Aromas may benaturally clustered into three groups: piney/floral/fruity,grapefruit/citrus/licorice/lemon, and spicy/bitter, schematicallyrepresented by transparent ellipsoids. As can be seen in FIGS. 19B and19C, in many cases, the aromas having the same natural languagedescriptor are clustered in the same specific regions. The centroid ofeach aroma provides a map for direct correlation of sensor response tothe natural language descriptors. Once the centroid coordinates areknown, the model can be constructed which maps a vector of MQ-X gassensor responses to the most probable aromas associated with thoseresponses as described herein.

FIG. 19D-19F illustrate similar graphs as described above for thenormalized MQ-7 and MQ-2 sensor responses over 18 days of exposure tohops aromas. FIG. 19G-19I illustrate similar graphs as described abovefor the normalized MQ-5 and MQ-2 sensor responses over 18 days ofexposure to hops aromas. As can be seen in FIGS. 19B, 19C, 19E, 19F, 19Hand 19I, in many cases, the aromas having the same natural languagedescriptor are clustered in the same specific regions. The centroid ofeach aroma provides a map for direct correlation of sensor response tothe natural language descriptors. Once the centroid coordinates areknown, the model can be constructed which maps a vector of MQ-X gassensor responses to the most probable aromas associated with thoseresponses as described herein. This also suggests that differentcombinations, such as ratios of the sensor outputs and/or ratios incombinations with absolute individual sensor responses, may be used toincrease the clustering and differentiation.

In an aspect of the disclosure, when the model is trained and meets theperformance requirement(s), the user may press a button to indicate thatthe system (e.g., 1A) is ready for prediction (e.g., prediction).

FIG. 20 illustrates a method in accordance with aspects of thedisclosure. This method may be used to predict the natural languagedescriptors associated with an aroma and a confidence. At S1715, an itemmay be brought near the housing 850 and held below the air channel toallow thermal convection from high temperature of sensors to draft airwith aroma into. In other aspects, a fan may be used to move the airinto the air channel. For each sensor in the array the rise time of thesignal as well as a value of stabilized signal response are measured.The single board computer 20A receives the output from the sensor array12. The single board computer 20A retrieves the trained and testedlogistic regression model at 52000. At 52005, the single board computer20A uses the model to predict the natural language descriptorsassociated with an aroma and a confidence. When the model uses ratios ofthe sensor output, the single board computer 20A calculates theratio(s). The model outputs the probabilities for natural languagedescriptors. For example, the model may be trained to recognize threeidentifiers: [sweet, sour, bitter]. In this case, the model may thenoutput an array such as OUTPUT=[0.25, 0.7, 0.05]. In this case, themodel is predicting that there is 25% chance of sweet aroma, 70% of souraroma, and 5% of bitter aroma. This information tells us the percentconfidence in each detected aroma, and which aroma was the primary aroma(the aroma with the highest percentage), and which other secondaryaromas were detected. When there are multiple natural languagedescriptors used in the training, any natural language descriptor thatis not predicted may have a value of 0, which means that the aroma isnot detected with any degree of confidence. For example, the output maybe OUTPUT=[[0.25, 0.00, 0.00, 0.7, 0.00, 0.00, 0.05]. In this example,four natural language descriptors of the seven natural languagedescriptors are not predicted with any degree of confidence. In somecases, the model might predict an OUTPUT=[0.96, 0.01, 0.03]. In thiscase, there is one dominant/primary aroma (with 96% probability), e.g.,sweet, and that the other aromas, e.g., sour and bitter aromas were notdetected with any degree of confidence (only 2% and 3% respectively).

At S1745B, the single board computer 20A may output the results of theprediction. In some aspects of the disclosure, the single board computer20A may cause the results to be displayed on a display. The percentagesmay be displaying in order of confidence. For example, the naturallanguage descriptor with the highest confidence may be displayed first.Using the above example where the OUTPUT=[0.25, 0.7, 0.05], the displaymay display, 70% sour aroma, 25% sweet aroma and 5% bitter aroma. Inother aspects, the display may only display the primary natural languagedescriptor. In other aspects, there may be a confidence threshold, andthe display may only display natural language descriptors having aconfidence above the confidence threshold. A user may set the confidencethreshold. In other aspects, the display may display all of the naturallanguage descriptors having a confidence above zero (or all descriptorswith the respective confidence including zero). Instead of and/or inaddition to displaying the natural language descriptors and confidence,the single board computer 20A may transmit the same to a device via textor an email.

At S2010, the single board computer 20A may determine the percentremaining or depletion of the aroma(s). In an aspect of the disclosure,the user may input into the system 1A the type of the item that is thetarget of the determination. Similar to above, the user may use thetouch panel display to input the type. As noted above, in atraining/testing mode, measurements may be done for samples which areallowed to degrade their aroma (for instance samples are kept in theopen air for day, two . . . week). Based on the input type, the singleboard computer 20A may retrieve the dataset for the type. The output ofone or more sensors or ratios of the output of one or more sensorshaving the largest changes over the measurement may be selected toevaluate the degradation of the aroma (depletion or remaining percent).The change may be plotted in x and y coordinates for visualizationand/or interpolation.

FIG. 21 illustrates an example sensor ratio changing over 5 days ofmeasurements. The sensors are identified as S1 and S2 (ratio S1/S2).However, as noted above, individual sensor output may also be used. Thetime is on the x-axis and ratio value is on the y-axis. A reference,e.g., 100% maximum aroma may be determined. In an aspect of thedisclosure, the manufacturer of the item may provide an aroma/freshnessindex 2100A. A dot on the y-axis at T=0 shown in FIG. 21 represents thevalue.

The index may include information of a single sensor, multiple sensorsand/or one or more ratios. This is the measurement when the item ismanufactured (new). In other aspects of the disclosure, when the item isfirst opened and its initial measurements are obtained by the system(e.g., 1A), these measurements may be used as the maximum reference2100B. A dot which intersects a vertical dashed line represents thisvalue. (day 1 measurement). Dots (circles) are also shown representingthe ratio of sensor outputs in day 2-5 (ratio decreases). As can be seenin FIG. 21, the change is non-linear. To approximate full degradation(depletion and zero remaining), both linear and polynomial fit may beused. The linear extrapolation 2105A is shown at the end of a dashedstraight line and the polynomial fit extrapolation 2105B is shown to theleft of the linear extrapolation 2105A. The signature for S1/S2representing fully depletion aroma 2105C may be obtained from averaging2105A and 2105B (+−error). Full depletion aroma 2105C is shown on thex-axis with a dot (the outputs of the sensors effectively would equalthe background).

Also, once the linear extrapolation 2105A and the Polynomial fitextrapolation 52105B is determined, when the alternative reference 2100Bis used, linear extrapolation and polynomial fit may be used todetermine the ratio at T=0. The signature 2115 at T=0 may be an averageof the two (+−error).

FIG. 21 also shows an arbitrary ratio representing a ratio of measuredsensor output at an unknown time, e.g., S1/S2_(pd) 2120. Once the 2105Cand 2115 are determined, the time on the x-axis associated with theratio of the sensor output may be determined using interpolation.

The time (pd) (partial depletion or remaining amount) is shown from boththe manufacture aroma index 2100A and the alternative reference 2100B(e.g., 2115A and 2125B). This may be the predicted age of the item.

The remaining amount of the aroma(s) from a single sensor or ratio maybe determined from the current sensor output or ratio divided by thedifference between the sensor output or ratio at both T=0 (either 2100Aor 2115) and T=full depletion (2105C) times 100%. The partial depletionis the complement (1-remaining amount). For example if the ratio(S1/S2)_(pd) is 20% than there is a 80% partial depletion.

The above process may be repeated for each selected sensor output andratio, e.g., determining 2100A or 2100B, 2105A, 2105B, 2105C (andcorresponding total depletion time), 2115 (if 2100A is not available),and time_(pd) 2125A and 2125B

Similar to above, a remaining amount of the aroma(s) may be calculatedfrom an average of each individual determination (replace the 2100A or2115 and 2105C with the average and use the average sensor outputs orratios of the outputs). When the standard deviation is larger than firstpercentage, it may be an indication that there is a larger discrepancyin sensor reading and need to down select a lesser number of sensoroutputs and/or ratios to average. The down selection may continue untilthe standard deviation is less than a second percentage. In an aspect ofthe disclosure, the first percentage may be 15% and the second may be10%.

As used herein terms such as “a”, “an” and “the” are not intended torefer to only a singular entity, but include the general class of whicha specific example may be used for illustration.

As used herein, terms defined in the singular are intended to includethose terms defined in the plural and vice versa.

References in the specification to “one aspect”, “certain aspects”,“some aspects” or “an aspect”, indicate that the aspect(s) described mayinclude a particular feature or characteristic, but every aspect may notnecessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same aspect.

Various aspects of the present disclosure may be embodied as a program,software, or computer instructions embodied or stored in a computer ormachine usable or readable medium, or a group of media which causes thecomputer or machine to perform the steps of the method when executed onthe computer, processor, and/or machine. A program storage devicereadable by a machine, e.g., a computer readable medium, tangiblyembodying a program of instructions executable by the machine to performvarious functionalities and methods described in the present disclosureis also provided, e.g., a computer program product.

The computer readable medium could be a computer readable storage deviceor a computer readable signal medium. A computer readable storage devicemay be, for example, a magnetic, optical, electronic, electromagnetic,infrared, or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing; however, the computer readable storagedevice is not limited to these examples except a computer readablestorage device excludes computer readable signal medium. Additionalexamples of the computer readable storage device can include: a portablecomputer diskette, a hard disk, a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical storage device, or any appropriatecombination of the foregoing; however, the computer readable storagedevice is also not limited to these examples. Any tangible medium thatcan contain, or store, a program for use by or in connection with aninstruction execution system, apparatus, or device could be a computerreadable storage device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, such as, but notlimited to, in baseband or as part of a carrier wave. A propagatedsignal may take any of a plurality of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium(exclusive of computer readable storage device) that can communicate,propagate, or transport a program for use by or in connection with asystem, apparatus, or device. Program code embodied on a computerreadable signal medium may be transmitted using any appropriate medium,including but not limited to wireless, wired, optical fiber cable, RF,etc., or any suitable combination of the foregoing.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting the scope of thedisclosure and is not intended to be exhaustive. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure.

What is claimed is:
 1. A system comprising: an electronic nose (e-nose)comprising: a plurality of thin film gas sensors; a first processorconfigured to supply power to the plurality of thin film gas sensors tobias the sensors and receive output from each of the plurality of thinfilm gas sensors; and a second processor configured to: receive theoutput from each of the plurality of thin film gas sensors, the outputfrom each of the plurality of thin film gas sensors being in response todifferent analytes separately positioned near the plurality of thin filmgas sensors, respectively, one at a time, and different combinations ofanalytes positioned near the plurality of thin film gas sensors,respectively, one at a time; generate randomly a first dataset fortraining and a second dataset for testing a plurality of models usingthe received output; train and test the plurality of models using one ormore combinations of outputs from the plurality of thin film gassensors, the plurality of models are generated using a plurality ofdifferent machine learning techniques, the training based on the firstdataset and the testing based on the second dataset; evaluate aprediction accuracy of each of the plurality of models using anevaluation parameter and select a model from among the plurality ofmodels to deploy for detecting analytes based on a comparison of theevaluation parameter for each of the plurality of models; receive, anoutput of each of the plurality of thin film gas sensors caused byunknown one or more analytes; and predict, using the deployed model, theone or more analytes that causes the output.
 2. The system of claim 1,wherein the output from each of the plurality of thin film gas sensorsbeing in response to different concentrations of analytes separatelypositioned near the plurality of thin film gas sensors, respectively,one at a time, and different combinations of concentrations of differentanalytes positioned near the plurality of thin film gas sensors,respectively, one at a time, and wherein the second processor isconfigured to: generate the first dataset and the second dataset usingthe outputs corresponding to the different concentrations, train andtest the plurality of models using the first dataset and the seconddataset using the outputs corresponding to the different concentrations;evaluate a prediction accuracy of each of the plurality of models usingan evaluation parameter and select a model from among the plurality ofmodels to deploy for detecting concentrations of analytes based on acomparison of the evaluation parameter for each of the plurality ofmodels; receive, an output of each of the plurality of thin film gassensors caused by unknown concentrations of one or more analytesadjacent to the e-nose; and predict, using the deployed model, theconcentrations of the one or more analytes that causes the output. 3.The system of claim 2, wherein a different model is selected forpredicting the concentration and predicting the type.
 4. The system ofclaim 1, wherein each of the plurality of thin film gas sensors aretuned to detect one or more analytes and at least one analytes used inthe training or testing is not one of the one or more analytes that anyof the plurality of thin film gas sensors are tuned to detect.
 5. Thesystem of claim 1, wherein the e-nose further comprises a mountingbracket for mounting to an air flow passage, the mounting bracket beingconfigured to hold the plurality of thin film gas sensors such that eachactive sensor portion is within the air flow passage.
 6. The system ofclaim 5, further comprising an assembly configured to be attached to anexhaust or duct of a ventilation system, wherein the assembly hasopenings on corresponding ends thereof to enable air flow, the assemblyis configured to hold the plurality of thin film gas sensors such thateach active sensor portion is within the exhaust or the duct.
 7. Thesystem of claim 5, wherein the e-nose further comprises a wirelesscommunication interface and the wireless communication interfacetransmits the output from each of the plurality of thin film gas sensorsto the first processor.
 8. The system of claim 1, wherein the e-nosefurther comprising a housing, the housing has openings on correspondingends thereof to enable air flow, the housing having an air channel forair to flow between the ends, wherein the plurality of thin film gassensors are positioned such that each active sensor portion is withinthe air channel and corresponding circuitry is external to the airchannel.
 9. The system of claim 8, wherein the first processor and thesecond processor are the same processor, wherein the housing has thesame processor, the same processor being outside the pipe.
 10. Thesystem of claim 9, wherein the e-nose is portable.
 11. The system ofclaim 9, wherein the housing is mountable to a wall of a chemicalcabinet or wall of a room.
 12. The system of claim 2, further comprisinga memory, wherein the second processor stores the predicted relativeconcentration in the memory with a timestamp.
 13. The system of claim12, wherein the second processor is further configured to determinewhether an event has occurred based on at least one of the predictedrelative concentration or a change in the predicted relativeconcentration over time.
 14. The system of claim 13, wherein the secondprocessor is further configured to transmit an alert when the event hasoccurred.
 15. The system of claim 14, wherein the event is a spillage ofa chemical and the alert is transmitted to a security system.
 16. Thesystem of claim 9, wherein the housing further comprise a camera and thesame processor is further configured to activate the camera when anevent is determined.
 17. The system of claim 13, wherein the event is anabnormal breathing pattern, and the alert is transmitted to a medicalservice provider.
 18. The system of claim 1, further comprising a switchcoupled to a power source and the plurality of thin film gas sensors,wherein the first processor periodically controls the switch to close tosupply power from the power source to the plurality of thin film gassensors and be opened otherwise.
 19. The system of claim 18, furthercomprising a graphic user interface configured to receive the period andto display the output from each of the plurality of thin film gassensors.
 20. The system of claim 1, wherein the system comprises aplurality of the e-noses, each transmitting output from the plurality ofthin film gas sensors to the second processor, and wherein the secondprocessor is configured to determine a location of an event based onlocations of the plurality of the e-noses.